Overview

Dataset statistics

Number of variables32
Number of observations8124
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory256.0 B

Variable types

Numeric17
Categorical13
Boolean2

Warnings

post_code has a high cardinality: 8050 distinct values High cardinality
post_area has a high cardinality: 1937 distinct values High cardinality
Balance_Transfer is highly correlated with Life_Insurance and 3 other fieldsHigh correlation
Term_Deposit is highly correlated with Medical_Insurance and 1 other fieldsHigh correlation
Life_Insurance is highly correlated with Balance_Transfer and 6 other fieldsHigh correlation
Medical_Insurance is highly correlated with Term_Deposit and 1 other fieldsHigh correlation
Average_A/C_Balance is highly correlated with Life_Insurance and 4 other fieldsHigh correlation
Personal_Loan is highly correlated with Investment_in_Equity and 2 other fieldsHigh correlation
Investment_in_Mutual_Fund is highly correlated with Life_Insurance and 4 other fieldsHigh correlation
Online_Purchase_Amount is highly correlated with Investment_in_EquityHigh correlation
Investment_in_Commudity is highly correlated with Balance_Transfer and 8 other fieldsHigh correlation
Investment_in_Equity is highly correlated with Life_Insurance and 7 other fieldsHigh correlation
Investment_in_Derivative is highly correlated with Balance_Transfer and 7 other fieldsHigh correlation
Portfolio_Balance is highly correlated with Balance_Transfer and 7 other fieldsHigh correlation
Average_Credit_Card_Transaction is highly correlated with Investment_in_CommudityHigh correlation
Balance_Transfer is highly correlated with Life_Insurance and 2 other fieldsHigh correlation
Term_Deposit is highly correlated with Investment_in_CommudityHigh correlation
Life_Insurance is highly correlated with Balance_Transfer and 5 other fieldsHigh correlation
Medical_Insurance is highly correlated with Investment_in_CommudityHigh correlation
Average_A/C_Balance is highly correlated with Life_Insurance and 3 other fieldsHigh correlation
Personal_Loan is highly correlated with Investment_in_EquityHigh correlation
Investment_in_Mutual_Fund is highly correlated with Investment_in_Equity and 2 other fieldsHigh correlation
Online_Purchase_Amount is highly correlated with Investment_in_EquityHigh correlation
Investment_in_Commudity is highly correlated with Average_Credit_Card_Transaction and 7 other fieldsHigh correlation
Investment_in_Equity is highly correlated with Life_Insurance and 7 other fieldsHigh correlation
Investment_in_Derivative is highly correlated with Life_Insurance and 5 other fieldsHigh correlation
Portfolio_Balance is highly correlated with Balance_Transfer and 6 other fieldsHigh correlation
Balance_Transfer is highly correlated with Investment_in_CommudityHigh correlation
Life_Insurance is highly correlated with Investment_in_Commudity and 1 other fieldsHigh correlation
Average_A/C_Balance is highly correlated with Investment_in_Equity and 1 other fieldsHigh correlation
Investment_in_Mutual_Fund is highly correlated with Investment_in_Equity and 1 other fieldsHigh correlation
Investment_in_Commudity is highly correlated with Balance_Transfer and 3 other fieldsHigh correlation
Investment_in_Equity is highly correlated with Average_A/C_Balance and 3 other fieldsHigh correlation
Investment_in_Derivative is highly correlated with Life_Insurance and 5 other fieldsHigh correlation
Portfolio_Balance is highly correlated with Investment_in_Commudity and 2 other fieldsHigh correlation
Investment_in_Mutual_Fund is highly correlated with Personal_Loan and 6 other fieldsHigh correlation
Personal_Loan is highly correlated with Investment_in_Mutual_Fund and 6 other fieldsHigh correlation
Investment_Tax_Saving_Bond is highly correlated with Investment_in_Equity and 1 other fieldsHigh correlation
family_income is highly correlated with year_last_moved and 2 other fieldsHigh correlation
TVarea is highly correlated with regionHigh correlation
home_status is highly correlated with year_last_moved and 2 other fieldsHigh correlation
occupation_partner is highly correlated with status and 2 other fieldsHigh correlation
year_last_moved is highly correlated with family_income and 2 other fieldsHigh correlation
Investment_in_Derivative is highly correlated with Investment_in_Mutual_Fund and 7 other fieldsHigh correlation
status is highly correlated with family_income and 3 other fieldsHigh correlation
gender is highly correlated with occupation_partnerHigh correlation
Balance_Transfer is highly correlated with Investment_in_Mutual_Fund and 6 other fieldsHigh correlation
Average_Credit_Card_Transaction is highly correlated with Home_Loan and 1 other fieldsHigh correlation
occupation is highly correlated with occupation_partner and 1 other fieldsHigh correlation
Average_A/C_Balance is highly correlated with Investment_in_Derivative and 1 other fieldsHigh correlation
Home_Loan is highly correlated with Average_Credit_Card_TransactionHigh correlation
Portfolio_Balance is highly correlated with Investment_in_Mutual_Fund and 7 other fieldsHigh correlation
Investment_in_Equity is highly correlated with Investment_in_Mutual_Fund and 8 other fieldsHigh correlation
children is highly correlated with age_bandHigh correlation
Online_Purchase_Amount is highly correlated with Investment_Tax_Saving_Bond and 1 other fieldsHigh correlation
age_band is highly correlated with family_income and 5 other fieldsHigh correlation
region is highly correlated with TVareaHigh correlation
Life_Insurance is highly correlated with Investment_in_Mutual_Fund and 6 other fieldsHigh correlation
Investment_in_Commudity is highly correlated with Investment_in_Mutual_Fund and 7 other fieldsHigh correlation
TVarea is highly correlated with regionHigh correlation
region is highly correlated with TVareaHigh correlation
Personal_Loan is highly skewed (γ1 = 26.15959592) Skewed
Online_Purchase_Amount is highly skewed (γ1 = 21.76395425) Skewed
REF_NO is uniformly distributed Uniform
post_code is uniformly distributed Uniform
REF_NO has unique values Unique
Average_Credit_Card_Transaction has 4989 (61.4%) zeros Zeros
Balance_Transfer has 3524 (43.4%) zeros Zeros
Term_Deposit has 4587 (56.5%) zeros Zeros
Life_Insurance has 2454 (30.2%) zeros Zeros
Medical_Insurance has 4046 (49.8%) zeros Zeros
Average_A/C_Balance has 2806 (34.5%) zeros Zeros
Personal_Loan has 5134 (63.2%) zeros Zeros
Investment_in_Mutual_Fund has 2602 (32.0%) zeros Zeros
Investment_Tax_Saving_Bond has 5133 (63.2%) zeros Zeros
Home_Loan has 5609 (69.0%) zeros Zeros
Online_Purchase_Amount has 5700 (70.2%) zeros Zeros
Investment_in_Commudity has 825 (10.2%) zeros Zeros
Investment_in_Equity has 915 (11.3%) zeros Zeros
Investment_in_Derivative has 445 (5.5%) zeros Zeros

Reproduction

Analysis started2021-09-27 09:24:04.155155
Analysis finished2021-09-27 09:25:42.079448
Duration1 minute and 37.92 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

REF_NO
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct8124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5797.343304
Minimum2
Maximum11518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:42.278810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile592.15
Q12924.75
median5811.5
Q38681.5
95-th percentile10947.85
Maximum11518
Range11516
Interquartile range (IQR)5756.75

Descriptive statistics

Standard deviation3322.497568
Coefficient of variation (CV)0.5731069205
Kurtosis-1.200526797
Mean5797.343304
Median Absolute Deviation (MAD)2878.5
Skewness-0.01280283725
Sum47097617
Variance11038990.09
MonotonicityNot monotonic
2021-09-27T14:55:42.571725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
27241
 
< 0.1%
27481
 
< 0.1%
109361
 
< 0.1%
47911
 
< 0.1%
68381
 
< 0.1%
27401
 
< 0.1%
109281
 
< 0.1%
47831
 
< 0.1%
68301
 
< 0.1%
Other values (8114)8114
99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
111
< 0.1%
141
< 0.1%
161
< 0.1%
181
< 0.1%
ValueCountFrequency (%)
115181
< 0.1%
115141
< 0.1%
115131
< 0.1%
115121
< 0.1%
115111
< 0.1%
115071
< 0.1%
115061
< 0.1%
115031
< 0.1%
115021
< 0.1%
115011
< 0.1%

children
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Zero
4991 
1
1474 
2
1271 
3
 
375
4+
 
13

Length

Max length4
Median length4
Mean length2.844657804
Min length1

Characters and Unicode

Total characters23110
Distinct characters9
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd rowZero
3rd row1
4th row2
5th rowZero

Common Values

ValueCountFrequency (%)
Zero4991
61.4%
11474
 
18.1%
21271
 
15.6%
3375
 
4.6%
4+13
 
0.2%

Length

2021-09-27T14:55:43.124666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T14:55:43.307504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
zero4991
61.4%
11474
 
18.1%
21271
 
15.6%
3375
 
4.6%
413
 
0.2%

Most occurring characters

ValueCountFrequency (%)
Z4991
21.6%
e4991
21.6%
r4991
21.6%
o4991
21.6%
11474
 
6.4%
21271
 
5.5%
3375
 
1.6%
413
 
0.1%
+13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14973
64.8%
Uppercase Letter4991
 
21.6%
Decimal Number3133
 
13.6%
Math Symbol13
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11474
47.0%
21271
40.6%
3375
 
12.0%
413
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e4991
33.3%
r4991
33.3%
o4991
33.3%
Uppercase Letter
ValueCountFrequency (%)
Z4991
100.0%
Math Symbol
ValueCountFrequency (%)
+13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin19964
86.4%
Common3146
 
13.6%

Most frequent character per script

Common
ValueCountFrequency (%)
11474
46.9%
21271
40.4%
3375
 
11.9%
413
 
0.4%
+13
 
0.4%
Latin
ValueCountFrequency (%)
Z4991
25.0%
e4991
25.0%
r4991
25.0%
o4991
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII23110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Z4991
21.6%
e4991
21.6%
r4991
21.6%
o4991
21.6%
11474
 
6.4%
21271
 
5.5%
3375
 
1.6%
413
 
0.1%
+13
 
0.1%

age_band
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
45-50
1098 
41-45
903 
36-40
895 
55-60
865 
31-35
840 
Other values (8)
3523 

Length

Max length7
Median length5
Mean length4.927868045
Min length3

Characters and Unicode

Total characters40034
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31-35
2nd row45-50
3rd row36-40
4th row31-35
5th row55-60

Common Values

ValueCountFrequency (%)
45-501098
13.5%
41-45903
11.1%
36-40895
11.0%
55-60865
10.6%
31-35840
10.3%
51-55833
10.3%
26-30735
9.0%
61-65700
8.6%
65-70468
5.8%
22-25356
 
4.4%
Other values (3)431
 
5.3%

Length

2021-09-27T14:55:43.838510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
45-501098
13.5%
41-45903
11.1%
36-40895
11.0%
55-60865
10.6%
31-35840
10.3%
51-55833
10.3%
26-30735
9.0%
61-65700
8.6%
65-70468
5.8%
22-25356
 
4.4%
Other values (3)431
 
5.3%

Most occurring characters

ValueCountFrequency (%)
59692
24.2%
-7743
19.3%
64363
10.9%
04061
10.1%
43799
 
9.5%
13713
 
9.3%
33310
 
8.3%
21853
 
4.6%
7805
 
2.0%
+337
 
0.8%
Other values (6)358
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number31646
79.0%
Dash Punctuation7743
 
19.3%
Math Symbol337
 
0.8%
Lowercase Letter264
 
0.7%
Uppercase Letter44
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
59692
30.6%
64363
13.8%
04061
12.8%
43799
 
12.0%
13713
 
11.7%
33310
 
10.5%
21853
 
5.9%
7805
 
2.5%
850
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
n132
50.0%
k44
 
16.7%
o44
 
16.7%
w44
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
-7743
100.0%
Math Symbol
ValueCountFrequency (%)
+337
100.0%
Uppercase Letter
ValueCountFrequency (%)
U44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common39726
99.2%
Latin308
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
59692
24.4%
-7743
19.5%
64363
11.0%
04061
10.2%
43799
 
9.6%
13713
 
9.3%
33310
 
8.3%
21853
 
4.7%
7805
 
2.0%
+337
 
0.8%
Latin
ValueCountFrequency (%)
n132
42.9%
U44
 
14.3%
k44
 
14.3%
o44
 
14.3%
w44
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII40034
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59692
24.2%
-7743
19.3%
64363
10.9%
04061
10.1%
43799
 
9.5%
13713
 
9.3%
33310
 
8.3%
21853
 
4.6%
7805
 
2.0%
+337
 
0.8%
Other values (6)358
 
0.9%

status
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Partner
6124 
Single/Never Married
881 
Divorced/Separated
 
569
Widowed
 
510
Unknown
 
40

Length

Max length20
Median length7
Mean length9.180206795
Min length7

Characters and Unicode

Total characters74580
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPartner
2nd rowPartner
3rd rowPartner
4th rowPartner
5th rowPartner

Common Values

ValueCountFrequency (%)
Partner6124
75.4%
Single/Never Married881
 
10.8%
Divorced/Separated569
 
7.0%
Widowed510
 
6.3%
Unknown40
 
0.5%

Length

2021-09-27T14:55:44.410993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T14:55:44.601969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
partner6124
68.0%
married881
 
9.8%
single/never881
 
9.8%
divorced/separated569
 
6.3%
widowed510
 
5.7%
unknown40
 
0.4%

Most occurring characters

ValueCountFrequency (%)
r16029
21.5%
e11865
15.9%
a8143
10.9%
n7125
9.6%
t6693
9.0%
P6124
 
8.2%
d3039
 
4.1%
i2841
 
3.8%
v1450
 
1.9%
/1450
 
1.9%
Other values (14)9821
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter61794
82.9%
Uppercase Letter10455
 
14.0%
Other Punctuation1450
 
1.9%
Space Separator881
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r16029
25.9%
e11865
19.2%
a8143
13.2%
n7125
11.5%
t6693
10.8%
d3039
 
4.9%
i2841
 
4.6%
v1450
 
2.3%
o1119
 
1.8%
g881
 
1.4%
Other values (5)2609
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
P6124
58.6%
S1450
 
13.9%
N881
 
8.4%
M881
 
8.4%
D569
 
5.4%
W510
 
4.9%
U40
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/1450
100.0%
Space Separator
ValueCountFrequency (%)
881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72249
96.9%
Common2331
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r16029
22.2%
e11865
16.4%
a8143
11.3%
n7125
9.9%
t6693
9.3%
P6124
 
8.5%
d3039
 
4.2%
i2841
 
3.9%
v1450
 
2.0%
S1450
 
2.0%
Other values (12)7490
10.4%
Common
ValueCountFrequency (%)
/1450
62.2%
881
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII74580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r16029
21.5%
e11865
15.9%
a8143
10.9%
n7125
9.6%
t6693
9.0%
P6124
 
8.2%
d3039
 
4.1%
i2841
 
3.8%
v1450
 
1.9%
/1450
 
1.9%
Other values (14)9821
13.2%

occupation
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Professional
1949 
Retired
1799 
Secretarial/Admin
1435 
Housewife
984 
Business Manager
578 
Other values (4)
1379 

Length

Max length17
Median length12
Mean length11.07520926
Min length5

Characters and Unicode

Total characters89975
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProfessional
2nd rowSecretarial/Admin
3rd rowManual Worker
4th rowManual Worker
5th rowHousewife

Common Values

ValueCountFrequency (%)
Professional1949
24.0%
Retired1799
22.1%
Secretarial/Admin1435
17.7%
Housewife984
12.1%
Business Manager578
 
7.1%
Manual Worker451
 
5.6%
Unknown449
 
5.5%
Other432
 
5.3%
Student47
 
0.6%

Length

2021-09-27T14:55:45.193193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T14:55:45.376810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
professional1949
21.3%
retired1799
19.7%
secretarial/admin1435
15.7%
housewife984
10.8%
business578
 
6.3%
manager578
 
6.3%
worker451
 
4.9%
manual451
 
4.9%
unknown449
 
4.9%
other432
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e12471
13.9%
r8530
 
9.5%
i8180
 
9.1%
a6877
 
7.6%
s6616
 
7.4%
n6385
 
7.1%
o5782
 
6.4%
l3835
 
4.3%
t3760
 
4.2%
d3281
 
3.6%
Other values (20)24258
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter76923
85.5%
Uppercase Letter10588
 
11.8%
Other Punctuation1435
 
1.6%
Space Separator1029
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e12471
16.2%
r8530
11.1%
i8180
10.6%
a6877
8.9%
s6616
8.6%
n6385
8.3%
o5782
7.5%
l3835
 
5.0%
t3760
 
4.9%
d3281
 
4.3%
Other values (8)11206
14.6%
Uppercase Letter
ValueCountFrequency (%)
P1949
18.4%
R1799
17.0%
S1482
14.0%
A1435
13.6%
M1029
9.7%
H984
9.3%
B578
 
5.5%
W451
 
4.3%
U449
 
4.2%
O432
 
4.1%
Other Punctuation
ValueCountFrequency (%)
/1435
100.0%
Space Separator
ValueCountFrequency (%)
1029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin87511
97.3%
Common2464
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e12471
14.3%
r8530
 
9.7%
i8180
 
9.3%
a6877
 
7.9%
s6616
 
7.6%
n6385
 
7.3%
o5782
 
6.6%
l3835
 
4.4%
t3760
 
4.3%
d3281
 
3.7%
Other values (18)21794
24.9%
Common
ValueCountFrequency (%)
/1435
58.2%
1029
41.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII89975
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e12471
13.9%
r8530
 
9.5%
i8180
 
9.1%
a6877
 
7.6%
s6616
 
7.4%
n6385
 
7.1%
o5782
 
6.4%
l3835
 
4.3%
t3760
 
4.2%
d3281
 
3.6%
Other values (20)24258
27.0%

occupation_partner
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Unknown
1942 
Professional
1620 
Retired
1558 
Manual Worker
1222 
Business Manager
575 
Other values (4)
1207 

Length

Max length17
Median length9
Mean length10.20396356
Min length5

Characters and Unicode

Total characters82897
Distinct characters30
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProfessional
2nd rowProfessional
3rd rowManual Worker
4th rowManual Worker
5th rowProfessional

Common Values

ValueCountFrequency (%)
Unknown1942
23.9%
Professional1620
19.9%
Retired1558
19.2%
Manual Worker1222
15.0%
Business Manager575
 
7.1%
Secretarial/Admin510
 
6.3%
Housewife422
 
5.2%
Other261
 
3.2%
Student14
 
0.2%

Length

2021-09-27T14:55:46.059793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T14:55:46.250790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
unknown1942
19.6%
professional1620
16.3%
retired1558
15.7%
worker1222
12.3%
manual1222
12.3%
business575
 
5.8%
manager575
 
5.8%
secretarial/admin510
 
5.1%
housewife422
 
4.3%
other261
 
2.6%

Most occurring characters

ValueCountFrequency (%)
n10342
12.5%
e9247
 
11.2%
r7478
 
9.0%
o6826
 
8.2%
a6234
 
7.5%
s5387
 
6.5%
i5195
 
6.3%
l3352
 
4.0%
k3164
 
3.8%
w2364
 
2.9%
Other values (20)23308
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter70159
84.6%
Uppercase Letter10431
 
12.6%
Space Separator1797
 
2.2%
Other Punctuation510
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n10342
14.7%
e9247
13.2%
r7478
10.7%
o6826
9.7%
a6234
8.9%
s5387
7.7%
i5195
7.4%
l3352
 
4.8%
k3164
 
4.5%
w2364
 
3.4%
Other values (8)10570
15.1%
Uppercase Letter
ValueCountFrequency (%)
U1942
18.6%
M1797
17.2%
P1620
15.5%
R1558
14.9%
W1222
11.7%
B575
 
5.5%
S524
 
5.0%
A510
 
4.9%
H422
 
4.0%
O261
 
2.5%
Space Separator
ValueCountFrequency (%)
1797
100.0%
Other Punctuation
ValueCountFrequency (%)
/510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin80590
97.2%
Common2307
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n10342
12.8%
e9247
11.5%
r7478
 
9.3%
o6826
 
8.5%
a6234
 
7.7%
s5387
 
6.7%
i5195
 
6.4%
l3352
 
4.2%
k3164
 
3.9%
w2364
 
2.9%
Other values (18)21001
26.1%
Common
ValueCountFrequency (%)
1797
77.9%
/510
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII82897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n10342
12.5%
e9247
 
11.2%
r7478
 
9.0%
o6826
 
8.2%
a6234
 
7.5%
s5387
 
6.5%
i5195
 
6.3%
l3352
 
4.0%
k3164
 
3.8%
w2364
 
2.9%
Other values (20)23308
28.1%

home_status
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Own Home
7506 
Rent from Council/HA
 
279
Rent Privately
 
205
Live in Parental Hom
 
90
Unclassified
 
44

Length

Max length20
Median length8
Mean length8.718119153
Min length8

Characters and Unicode

Total characters70826
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn Home
2nd rowOwn Home
3rd rowRent Privately
4th rowOwn Home
5th rowOwn Home

Common Values

ValueCountFrequency (%)
Own Home7506
92.4%
Rent from Council/HA279
 
3.4%
Rent Privately205
 
2.5%
Live in Parental Hom90
 
1.1%
Unclassified44
 
0.5%

Length

2021-09-27T14:55:46.909119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T14:55:47.092280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
home7506
45.0%
own7506
45.0%
rent484
 
2.9%
from279
 
1.7%
council/ha279
 
1.7%
privately205
 
1.2%
parental90
 
0.5%
live90
 
0.5%
hom90
 
0.5%
in90
 
0.5%

Most occurring characters

ValueCountFrequency (%)
8539
12.1%
n8493
12.0%
e8419
11.9%
o8154
11.5%
H7875
11.1%
m7875
11.1%
O7506
10.6%
w7506
10.6%
t779
 
1.1%
i752
 
1.1%
Other values (17)4928
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter45156
63.8%
Uppercase Letter16852
 
23.8%
Space Separator8539
 
12.1%
Other Punctuation279
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n8493
18.8%
e8419
18.6%
o8154
18.1%
m7875
17.4%
w7506
16.6%
t779
 
1.7%
i752
 
1.7%
l618
 
1.4%
r574
 
1.3%
a429
 
1.0%
Other values (7)1557
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
H7875
46.7%
O7506
44.5%
R484
 
2.9%
P295
 
1.8%
C279
 
1.7%
A279
 
1.7%
L90
 
0.5%
U44
 
0.3%
Space Separator
ValueCountFrequency (%)
8539
100.0%
Other Punctuation
ValueCountFrequency (%)
/279
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin62008
87.5%
Common8818
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n8493
13.7%
e8419
13.6%
o8154
13.1%
H7875
12.7%
m7875
12.7%
O7506
12.1%
w7506
12.1%
t779
 
1.3%
i752
 
1.2%
l618
 
1.0%
Other values (15)4031
6.5%
Common
ValueCountFrequency (%)
8539
96.8%
/279
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII70826
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8539
12.1%
n8493
12.0%
e8419
11.9%
o8154
11.5%
H7875
11.1%
m7875
11.1%
O7506
10.6%
w7506
10.6%
t779
 
1.1%
i752
 
1.1%
Other values (17)4928
7.0%

family_income
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
>=35,000
2014 
<27,500, >=25,000
969 
<30,000, >=27,500
796 
<25,000, >=22,500
656 
<12,500, >=10,000
535 
Other values (8)
3154 

Length

Max length17
Median length17
Mean length14.34908912
Min length7

Characters and Unicode

Total characters116572
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row>=35,000
2nd row>=35,000
3rd row<22,500, >=20,000
4th row<25,000, >=22,500
5th row>=35,000

Common Values

ValueCountFrequency (%)
>=35,0002014
24.8%
<27,500, >=25,000969
11.9%
<30,000, >=27,500796
 
9.8%
<25,000, >=22,500656
 
8.1%
<12,500, >=10,000535
 
6.6%
<20,000, >=17,500525
 
6.5%
<17,500, >=15,000521
 
6.4%
<15,000, >=12,500508
 
6.3%
<22,500, >=20,000479
 
5.9%
<10,000, >= 8,000452
 
5.6%
Other values (3)669
 
8.2%

Length

2021-09-27T14:55:47.634297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
35,0002014
13.2%
27,5001765
11.6%
25,0001625
10.7%
1341
8.8%
22,5001135
7.5%
17,5001046
6.9%
12,5001043
6.8%
15,0001029
6.8%
20,0001004
6.6%
10,000987
6.5%
Other values (4)2245
14.7%

Most occurring characters

ValueCountFrequency (%)
039153
33.6%
,19554
16.8%
59657
 
8.3%
>7783
 
6.7%
=7783
 
6.7%
27707
 
6.6%
7110
 
6.1%
<6002
 
5.1%
14105
 
3.5%
72811
 
2.4%
Other values (8)4907
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number67584
58.0%
Math Symbol21568
 
18.5%
Other Punctuation19554
 
16.8%
Space Separator7110
 
6.1%
Lowercase Letter648
 
0.6%
Uppercase Letter108
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039153
57.9%
59657
 
14.3%
27707
 
11.4%
14105
 
6.1%
72811
 
4.2%
32810
 
4.2%
8780
 
1.2%
4561
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n324
50.0%
k108
 
16.7%
o108
 
16.7%
w108
 
16.7%
Math Symbol
ValueCountFrequency (%)
>7783
36.1%
=7783
36.1%
<6002
27.8%
Other Punctuation
ValueCountFrequency (%)
,19554
100.0%
Space Separator
ValueCountFrequency (%)
7110
100.0%
Uppercase Letter
ValueCountFrequency (%)
U108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common115816
99.4%
Latin756
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
039153
33.8%
,19554
16.9%
59657
 
8.3%
>7783
 
6.7%
=7783
 
6.7%
27707
 
6.7%
7110
 
6.1%
<6002
 
5.2%
14105
 
3.5%
72811
 
2.4%
Other values (3)4151
 
3.6%
Latin
ValueCountFrequency (%)
n324
42.9%
U108
 
14.3%
k108
 
14.3%
o108
 
14.3%
w108
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII116572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039153
33.6%
,19554
16.8%
59657
 
8.3%
>7783
 
6.7%
=7783
 
6.7%
27707
 
6.6%
7110
 
6.1%
<6002
 
5.1%
14105
 
3.5%
72811
 
2.4%
Other values (8)4907
 
4.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
7543 
True
 
581
ValueCountFrequency (%)
False7543
92.8%
True581
 
7.2%
2021-09-27T14:55:47.796020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.1 KiB
False
7207 
True
917 
ValueCountFrequency (%)
False7207
88.7%
True917
 
11.3%
2021-09-27T14:55:47.899662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

year_last_moved
Real number (ℝ≥0)

HIGH CORRELATION

Distinct94
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1967.876908
Minimum0
Maximum1999
Zeros69
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:48.091092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1961
Q11978
median1988
Q31994
95-th percentile1998
Maximum1999
Range1999
Interquartile range (IQR)16

Descriptive statistics

Standard deviation182.5637854
Coefficient of variation (CV)0.09277195372
Kurtosis111.7581431
Mean1967.876908
Median Absolute Deviation (MAD)7
Skewness-10.63967102
Sum15987032
Variance33329.53575
MonotonicityNot monotonic
2021-09-27T14:55:48.390509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1997548
 
6.7%
1996525
 
6.5%
1998434
 
5.3%
1994423
 
5.2%
1995386
 
4.8%
1988338
 
4.2%
1993323
 
4.0%
1986316
 
3.9%
1992294
 
3.6%
1987284
 
3.5%
Other values (84)4253
52.4%
ValueCountFrequency (%)
069
0.8%
19012
 
< 0.1%
19022
 
< 0.1%
19031
 
< 0.1%
19042
 
< 0.1%
19053
 
< 0.1%
19061
 
< 0.1%
19072
 
< 0.1%
19083
 
< 0.1%
19091
 
< 0.1%
ValueCountFrequency (%)
199951
 
0.6%
1998434
5.3%
1997548
6.7%
1996525
6.5%
1995386
4.8%
1994423
5.2%
1993323
4.0%
1992294
3.6%
1991256
3.2%
1990273
3.4%

TVarea
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Central
1294 
Carlton
1237 
Meridian
977 
Yorkshire
847 
Granada
824 
Other values (9)
2945 

Length

Max length13
Median length7
Mean length7.43229936
Min length3

Characters and Unicode

Total characters60380
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMeridian
2nd rowMeridian
3rd rowHTV
4th rowScottish TV
5th rowYorkshire

Common Values

ValueCountFrequency (%)
Central1294
15.9%
Carlton1237
15.2%
Meridian977
12.0%
Yorkshire847
10.4%
Granada824
10.1%
HTV683
8.4%
Anglia597
7.3%
Tyne Tees433
 
5.3%
Scottish TV406
 
5.0%
TV South West286
 
3.5%
Other values (4)540
6.6%

Length

2021-09-27T14:55:48.950380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
central1294
13.6%
carlton1237
13.0%
meridian977
10.2%
yorkshire847
8.9%
granada824
8.6%
tv692
7.3%
htv683
7.2%
anglia597
 
6.3%
tyne433
 
4.5%
tees433
 
4.5%
Other values (7)1518
15.9%

Most occurring characters

ValueCountFrequency (%)
a6927
11.5%
r6488
 
10.7%
n5999
 
9.9%
e4914
 
8.1%
t4050
 
6.7%
i3979
 
6.6%
l3263
 
5.4%
o3006
 
5.0%
C2531
 
4.2%
T2241
 
3.7%
Other values (22)16982
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter47376
78.5%
Uppercase Letter11593
 
19.2%
Space Separator1411
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6927
14.6%
r6488
13.7%
n5999
12.7%
e4914
10.4%
t4050
8.5%
i3979
8.4%
l3263
6.9%
o3006
6.3%
s2107
 
4.4%
d1877
 
4.0%
Other values (9)4766
10.1%
Uppercase Letter
ValueCountFrequency (%)
C2531
21.8%
T2241
19.3%
V1375
11.9%
G999
 
8.6%
M977
 
8.4%
Y847
 
7.3%
S692
 
6.0%
H683
 
5.9%
A597
 
5.1%
U289
 
2.5%
Other values (2)362
 
3.1%
Space Separator
ValueCountFrequency (%)
1411
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58969
97.7%
Common1411
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6927
11.7%
r6488
11.0%
n5999
 
10.2%
e4914
 
8.3%
t4050
 
6.9%
i3979
 
6.7%
l3263
 
5.5%
o3006
 
5.1%
C2531
 
4.3%
T2241
 
3.8%
Other values (21)15571
26.4%
Common
ValueCountFrequency (%)
1411
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII60380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a6927
11.5%
r6488
 
10.7%
n5999
 
9.9%
e4914
 
8.1%
t4050
 
6.7%
i3979
 
6.6%
l3263
 
5.4%
o3006
 
5.0%
C2531
 
4.2%
T2241
 
3.7%
Other values (22)16982
28.1%

post_code
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8050
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
TF6 6LY
 
2
M14 7NQ
 
2
YO8 9JZ
 
2
DE11 0LF
 
2
CF39 0DB
 
2
Other values (8045)
8114 

Length

Max length8
Median length7
Mean length7.460118168
Min length6

Characters and Unicode

Total characters60606
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7976 ?
Unique (%)98.2%

Sample

1st rowM51 0GU
2nd rowL40 2AG
3rd rowTA19 9PT
4th rowFK2 9NG
5th rowLS23 7DJ

Common Values

ValueCountFrequency (%)
TF6 6LY2
 
< 0.1%
M14 7NQ2
 
< 0.1%
YO8 9JZ2
 
< 0.1%
DE11 0LF2
 
< 0.1%
CF39 0DB2
 
< 0.1%
YO32 3NS2
 
< 0.1%
CM12 0BA2
 
< 0.1%
SA2 0FN2
 
< 0.1%
CF48 1EW2
 
< 0.1%
LA12 7JP2
 
< 0.1%
Other values (8040)8104
99.8%

Length

2021-09-27T14:55:49.997258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr528
 
0.2%
wa422
 
0.1%
tq1222
 
0.1%
pr821
 
0.1%
le721
 
0.1%
pr421
 
0.1%
m1220
 
0.1%
ts520
 
0.1%
m1319
 
0.1%
hd719
 
0.1%
Other values (4888)16035
98.7%

Most occurring characters

ValueCountFrequency (%)
8124
 
13.4%
14089
 
6.7%
22848
 
4.7%
32429
 
4.0%
L2385
 
3.9%
N2142
 
3.5%
S2072
 
3.4%
B2048
 
3.4%
41975
 
3.3%
51897
 
3.1%
Other values (27)30597
50.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter31217
51.5%
Decimal Number21265
35.1%
Space Separator8124
 
13.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L2385
 
7.6%
N2142
 
6.9%
S2072
 
6.6%
B2048
 
6.6%
A1872
 
6.0%
E1863
 
6.0%
D1803
 
5.8%
P1738
 
5.6%
H1686
 
5.4%
T1606
 
5.1%
Other values (16)12002
38.4%
Decimal Number
ValueCountFrequency (%)
14089
19.2%
22848
13.4%
32429
11.4%
41975
9.3%
51897
8.9%
61856
8.7%
71651
7.8%
81572
 
7.4%
91521
 
7.2%
01427
 
6.7%
Space Separator
ValueCountFrequency (%)
8124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin31217
51.5%
Common29389
48.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
L2385
 
7.6%
N2142
 
6.9%
S2072
 
6.6%
B2048
 
6.6%
A1872
 
6.0%
E1863
 
6.0%
D1803
 
5.8%
P1738
 
5.6%
H1686
 
5.4%
T1606
 
5.1%
Other values (16)12002
38.4%
Common
ValueCountFrequency (%)
8124
27.6%
14089
13.9%
22848
 
9.7%
32429
 
8.3%
41975
 
6.7%
51897
 
6.5%
61856
 
6.3%
71651
 
5.6%
81572
 
5.3%
91521
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII60606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8124
 
13.4%
14089
 
6.7%
22848
 
4.7%
32429
 
4.0%
L2385
 
3.9%
N2142
 
3.5%
S2072
 
3.4%
B2048
 
3.4%
41975
 
3.3%
51897
 
3.1%
Other values (27)30597
50.5%

post_area
Categorical

HIGH CARDINALITY

Distinct1937
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
PR5
 
28
WA4
 
22
TQ12
 
22
LE7
 
21
PR4
 
21
Other values (1932)
8010 

Length

Max length4
Median length3
Mean length3.460118168
Min length2

Characters and Unicode

Total characters28110
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique449 ?
Unique (%)5.5%

Sample

1st rowM51
2nd rowL40
3rd rowTA19
4th rowFK2
5th rowLS23

Common Values

ValueCountFrequency (%)
PR528
 
0.3%
WA422
 
0.3%
TQ1222
 
0.3%
LE721
 
0.3%
PR421
 
0.3%
PR821
 
0.3%
M1220
 
0.2%
TS520
 
0.2%
M3319
 
0.2%
M1319
 
0.2%
Other values (1927)7911
97.4%

Length

2021-09-27T14:55:50.656272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pr528
 
0.3%
tq1222
 
0.3%
wa422
 
0.3%
pr421
 
0.3%
pr821
 
0.3%
le721
 
0.3%
ts520
 
0.2%
m1220
 
0.2%
m1319
 
0.2%
m3319
 
0.2%
Other values (1927)7911
97.4%

Most occurring characters

ValueCountFrequency (%)
13355
 
11.9%
22011
 
7.2%
31571
 
5.6%
L1348
 
4.8%
41243
 
4.4%
S1241
 
4.4%
N1094
 
3.9%
B1066
 
3.8%
61064
 
3.8%
51063
 
3.8%
Other values (26)13054
46.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14969
53.3%
Decimal Number13141
46.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L1348
 
9.0%
S1241
 
8.3%
N1094
 
7.3%
B1066
 
7.1%
A882
 
5.9%
E819
 
5.5%
T816
 
5.5%
P761
 
5.1%
D736
 
4.9%
C716
 
4.8%
Other values (16)5490
36.7%
Decimal Number
ValueCountFrequency (%)
13355
25.5%
22011
15.3%
31571
12.0%
41243
 
9.5%
61064
 
8.1%
51063
 
8.1%
7835
 
6.4%
8699
 
5.3%
0667
 
5.1%
9633
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin14969
53.3%
Common13141
46.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
L1348
 
9.0%
S1241
 
8.3%
N1094
 
7.3%
B1066
 
7.1%
A882
 
5.9%
E819
 
5.5%
T816
 
5.5%
P761
 
5.1%
D736
 
4.9%
C716
 
4.8%
Other values (16)5490
36.7%
Common
ValueCountFrequency (%)
13355
25.5%
22011
15.3%
31571
12.0%
41243
 
9.5%
61064
 
8.1%
51063
 
8.1%
7835
 
6.4%
8699
 
5.3%
0667
 
5.1%
9633
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII28110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13355
 
11.9%
22011
 
7.2%
31571
 
5.6%
L1348
 
4.8%
41243
 
4.4%
S1241
 
4.4%
N1094
 
3.9%
B1066
 
3.8%
61064
 
3.8%
51063
 
3.8%
Other values (26)13054
46.4%

Average_Credit_Card_Transaction
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1209
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.25109429
Minimum0
Maximum662.26
Zeros4989
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:50.951065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323.48
95-th percentile129.925
Maximum662.26
Range662.26
Interquartile range (IQR)23.48

Descriptive statistics

Standard deviation51.14749618
Coefficient of variation (CV)2.199788773
Kurtosis20.29545471
Mean23.25109429
Median Absolute Deviation (MAD)0
Skewness3.761152393
Sum188891.89
Variance2616.066366
MonotonicityNot monotonic
2021-09-27T14:55:51.221559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04989
61.4%
19.99112
 
1.4%
9.9986
 
1.1%
11.9954
 
0.7%
24.9948
 
0.6%
19.4946
 
0.6%
4.9943
 
0.5%
14.9943
 
0.5%
9.4940
 
0.5%
15.9939
 
0.5%
Other values (1199)2624
32.3%
ValueCountFrequency (%)
04989
61.4%
0.0131
 
0.4%
0.028
 
0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.5110
 
0.1%
0.524
 
< 0.1%
0.541
 
< 0.1%
1.031
 
< 0.1%
1.041
 
< 0.1%
ValueCountFrequency (%)
662.261
< 0.1%
592.361
< 0.1%
571.741
< 0.1%
565.361
< 0.1%
481.361
< 0.1%
477.821
< 0.1%
467.881
< 0.1%
461.931
< 0.1%
436.81
< 0.1%
421.761
< 0.1%

Balance_Transfer
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1860
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.08292221
Minimum0
Maximum2951.76
Zeros3524
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:51.507908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17.485
Q364.99
95-th percentile186.9005
Maximum2951.76
Range2951.76
Interquartile range (IQR)64.99

Descriptive statistics

Standard deviation79.08469243
Coefficient of variation (CV)1.716138835
Kurtosis231.6423454
Mean46.08292221
Median Absolute Deviation (MAD)17.485
Skewness8.173734355
Sum374377.66
Variance6254.388576
MonotonicityNot monotonic
2021-09-27T14:55:51.779593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03524
43.4%
0.01155
 
1.9%
24.99135
 
1.7%
29.99114
 
1.4%
19.9972
 
0.9%
34.9963
 
0.8%
25.9952
 
0.6%
0.5144
 
0.5%
44.9943
 
0.5%
0.0241
 
0.5%
Other values (1850)3881
47.8%
ValueCountFrequency (%)
03524
43.4%
0.01155
 
1.9%
0.0241
 
0.5%
0.0312
 
0.1%
0.042
 
< 0.1%
0.055
 
0.1%
0.461
 
< 0.1%
0.5144
 
0.5%
0.5235
 
0.4%
0.539
 
0.1%
ValueCountFrequency (%)
2951.761
< 0.1%
860.831
< 0.1%
749.381
< 0.1%
659.211
< 0.1%
644.871
< 0.1%
601.851
< 0.1%
596.851
< 0.1%
583.871
< 0.1%
573.41
< 0.1%
570.861
< 0.1%

Term_Deposit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1215
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.28464919
Minimum0
Maximum784.82
Zeros4587
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:52.079560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q334.49
95-th percentile124.957
Maximum784.82
Range784.82
Interquartile range (IQR)34.49

Descriptive statistics

Standard deviation54.13353666
Coefficient of variation (CV)1.984029052
Kurtosis28.6855796
Mean27.28464919
Median Absolute Deviation (MAD)0
Skewness4.174162624
Sum221660.49
Variance2930.439791
MonotonicityNot monotonic
2021-09-27T14:55:52.361742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04587
56.5%
24.99128
 
1.6%
29.99125
 
1.5%
19.9987
 
1.1%
14.9975
 
0.9%
9.9964
 
0.8%
34.9962
 
0.8%
0.0158
 
0.7%
29.4947
 
0.6%
24.4940
 
0.5%
Other values (1205)2851
35.1%
ValueCountFrequency (%)
04587
56.5%
0.0158
 
0.7%
0.0212
 
0.1%
0.031
 
< 0.1%
0.5128
 
0.3%
0.5210
 
0.1%
0.531
 
< 0.1%
1.021
 
< 0.1%
1.033
 
< 0.1%
1.61
 
< 0.1%
ValueCountFrequency (%)
784.821
< 0.1%
738.671
< 0.1%
716.121
< 0.1%
597.761
< 0.1%
539.181
< 0.1%
522.771
< 0.1%
514.261
< 0.1%
505.541
< 0.1%
493.781
< 0.1%
484.731
< 0.1%

Life_Insurance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2655
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.31793944
Minimum0
Maximum2930.41
Zeros2454
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:52.676184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.475
Q392.8875
95-th percentile239.874
Maximum2930.41
Range2930.41
Interquartile range (IQR)92.8875

Descriptive statistics

Standard deviation95.76245117
Coefficient of variation (CV)1.466097247
Kurtosis106.5999295
Mean65.31793944
Median Absolute Deviation (MAD)31.475
Skewness5.50937883
Sum530642.94
Variance9170.447054
MonotonicityNot monotonic
2021-09-27T14:55:52.936525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02454
30.2%
0.0177
 
0.9%
27.9958
 
0.7%
29.9952
 
0.6%
22.9947
 
0.6%
34.9944
 
0.5%
25.9944
 
0.5%
24.9940
 
0.5%
19.9938
 
0.5%
17.9938
 
0.5%
Other values (2645)5232
64.4%
ValueCountFrequency (%)
02454
30.2%
0.0177
 
0.9%
0.0225
 
0.3%
0.037
 
0.1%
0.081
 
< 0.1%
0.441
 
< 0.1%
0.5128
 
0.3%
0.5215
 
0.2%
0.536
 
0.1%
0.543
 
< 0.1%
ValueCountFrequency (%)
2930.411
< 0.1%
1005.531
< 0.1%
817.631
< 0.1%
799.121
< 0.1%
795.791
< 0.1%
774.61
< 0.1%
748.461
< 0.1%
734.11
< 0.1%
726.671
< 0.1%
719.61
< 0.1%

Medical_Insurance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1362
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.82619522
Minimum0
Maximum591.04
Zeros4046
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:53.236414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q326.97
95-th percentile81.4155
Maximum591.04
Range591.04
Interquartile range (IQR)26.97

Descriptive statistics

Standard deviation32.02233187
Coefficient of variation (CV)1.700945491
Kurtosis22.79540108
Mean18.82619522
Median Absolute Deviation (MAD)0.01
Skewness3.361940195
Sum152944.01
Variance1025.429738
MonotonicityNot monotonic
2021-09-27T14:55:53.500335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04046
49.8%
9.99168
 
2.1%
9.4961
 
0.8%
29.9956
 
0.7%
10.9954
 
0.7%
7.4954
 
0.7%
4.9951
 
0.6%
6.9951
 
0.6%
19.9948
 
0.6%
19.9844
 
0.5%
Other values (1352)3491
43.0%
ValueCountFrequency (%)
04046
49.8%
0.0130
 
0.4%
0.024
 
< 0.1%
0.481
 
< 0.1%
0.518
 
0.1%
0.524
 
< 0.1%
12
 
< 0.1%
1.491
 
< 0.1%
1.541
 
< 0.1%
1.981
 
< 0.1%
ValueCountFrequency (%)
591.041
< 0.1%
350.711
< 0.1%
306.851
< 0.1%
265.841
< 0.1%
244.31
< 0.1%
241.791
< 0.1%
235.361
< 0.1%
233.761
< 0.1%
233.381
< 0.1%
231.731
< 0.1%

Average_A/C_Balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1923
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.83802314
Minimum0
Maximum626.24
Zeros2806
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:53.789661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14.98
Q345.9225
95-th percentile120.692
Maximum626.24
Range626.24
Interquartile range (IQR)45.9225

Descriptive statistics

Standard deviation45.2494396
Coefficient of variation (CV)1.421238982
Kurtosis12.49559433
Mean31.83802314
Median Absolute Deviation (MAD)14.98
Skewness2.689985223
Sum258652.1
Variance2047.511784
MonotonicityNot monotonic
2021-09-27T14:55:54.069117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02806
34.5%
29.99112
 
1.4%
4.99111
 
1.4%
11.9996
 
1.2%
9.9984
 
1.0%
14.9974
 
0.9%
24.9969
 
0.8%
2.9949
 
0.6%
34.9948
 
0.6%
3.4941
 
0.5%
Other values (1913)4634
57.0%
ValueCountFrequency (%)
02806
34.5%
0.0127
 
0.3%
0.024
 
< 0.1%
0.051
 
< 0.1%
0.471
 
< 0.1%
0.519
 
0.1%
0.522
 
< 0.1%
0.971
 
< 0.1%
0.981
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
626.241
< 0.1%
415.141
< 0.1%
410.711
< 0.1%
402.61
< 0.1%
398.061
< 0.1%
380.751
< 0.1%
367.341
< 0.1%
351.381
< 0.1%
348.181
< 0.1%
347.791
< 0.1%

Personal_Loan
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1477
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.41582964
Minimum0
Maximum4905.93
Zeros5134
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:54.362579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320.8275
95-th percentile132.8025
Maximum4905.93
Range4905.93
Interquartile range (IQR)20.8275

Descriptive statistics

Standard deviation85.13014991
Coefficient of variation (CV)3.349493253
Kurtosis1354.453006
Mean25.41582964
Median Absolute Deviation (MAD)0
Skewness26.15959592
Sum206478.2
Variance7247.142423
MonotonicityNot monotonic
2021-09-27T14:55:54.663666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05134
63.2%
15.9947
 
0.6%
6.9935
 
0.4%
14.9930
 
0.4%
5.9929
 
0.4%
8.9927
 
0.3%
0.0126
 
0.3%
13.9926
 
0.3%
11.9925
 
0.3%
17.9923
 
0.3%
Other values (1467)2722
33.5%
ValueCountFrequency (%)
05134
63.2%
0.0126
 
0.3%
0.027
 
0.1%
0.516
 
0.1%
0.524
 
< 0.1%
0.532
 
< 0.1%
0.551
 
< 0.1%
0.992
 
< 0.1%
13
 
< 0.1%
1.021
 
< 0.1%
ValueCountFrequency (%)
4905.931
< 0.1%
1309.081
< 0.1%
1280.21
< 0.1%
1173.961
< 0.1%
898.391
< 0.1%
801.761
< 0.1%
772.021
< 0.1%
719.651
< 0.1%
704.341
< 0.1%
661.911
< 0.1%

Investment_in_Mutual_Fund
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2130
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.72362752
Minimum0
Maximum2561.27
Zeros2602
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:54.958494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.48
Q359.44
95-th percentile148.254
Maximum2561.27
Range2561.27
Interquartile range (IQR)59.44

Descriptive statistics

Standard deviation64.41602328
Coefficient of variation (CV)1.543873989
Kurtosis299.0911263
Mean41.72362752
Median Absolute Deviation (MAD)23.48
Skewness9.667247132
Sum338962.75
Variance4149.424055
MonotonicityNot monotonic
2021-09-27T14:55:55.251337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02602
32.0%
11.99111
 
1.4%
9.9981
 
1.0%
13.9970
 
0.9%
23.9864
 
0.8%
23.4849
 
0.6%
19.9847
 
0.6%
11.4947
 
0.6%
17.9939
 
0.5%
0.0139
 
0.5%
Other values (2120)4975
61.2%
ValueCountFrequency (%)
02602
32.0%
0.0139
 
0.5%
0.0215
 
0.2%
0.033
 
< 0.1%
0.061
 
< 0.1%
0.161
 
< 0.1%
0.481
 
< 0.1%
0.5120
 
0.2%
0.5211
 
0.1%
0.531
 
< 0.1%
ValueCountFrequency (%)
2561.271
< 0.1%
765.031
< 0.1%
648.541
< 0.1%
646.391
< 0.1%
633.891
< 0.1%
587.611
< 0.1%
576.611
< 0.1%
565.991
< 0.1%
5451
< 0.1%
522.581
< 0.1%

Investment_Tax_Saving_Bond
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct718
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.05724643
Minimum0
Maximum156.87
Zeros5133
Zeros (%)63.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:55.541338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.49
95-th percentile32.467
Maximum156.87
Range156.87
Interquartile range (IQR)5.49

Descriptive statistics

Standard deviation12.67337383
Coefficient of variation (CV)2.092266507
Kurtosis14.67481655
Mean6.05724643
Median Absolute Deviation (MAD)0
Skewness3.191713108
Sum49209.07
Variance160.6144043
MonotonicityNot monotonic
2021-09-27T14:55:55.817721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05133
63.2%
1218
 
2.7%
2107
 
1.3%
4.9998
 
1.2%
9.9980
 
1.0%
19.9969
 
0.8%
4.4962
 
0.8%
2.4959
 
0.7%
2.9948
 
0.6%
346
 
0.6%
Other values (708)2204
27.1%
ValueCountFrequency (%)
05133
63.2%
0.012
 
< 0.1%
0.12
 
< 0.1%
0.24
 
< 0.1%
0.452
 
< 0.1%
0.56
 
0.1%
0.652
 
< 0.1%
0.741
 
< 0.1%
0.851
 
< 0.1%
0.971
 
< 0.1%
ValueCountFrequency (%)
156.871
< 0.1%
138.561
< 0.1%
124.761
< 0.1%
121.441
< 0.1%
119.871
< 0.1%
102.461
< 0.1%
101.941
< 0.1%
101.811
< 0.1%
96.441
< 0.1%
95.921
< 0.1%

Home_Loan
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct760
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.416914082
Minimum0
Maximum162.35
Zeros5609
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:56.129526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.49
95-th percentile23.9785
Maximum162.35
Range162.35
Interquartile range (IQR)4.49

Descriptive statistics

Standard deviation9.945746638
Coefficient of variation (CV)2.251741024
Kurtosis26.08172109
Mean4.416914082
Median Absolute Deviation (MAD)0
Skewness3.977657035
Sum35883.01
Variance98.9178762
MonotonicityNot monotonic
2021-09-27T14:55:56.408242image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05609
69.0%
4.9991
 
1.1%
9.9977
 
0.9%
4.4973
 
0.9%
3.9954
 
0.7%
3.4946
 
0.6%
2.9939
 
0.5%
1.9937
 
0.5%
7.9936
 
0.4%
14.9936
 
0.4%
Other values (750)2026
 
24.9%
ValueCountFrequency (%)
05609
69.0%
0.018
 
0.1%
0.53
 
< 0.1%
0.511
 
< 0.1%
0.742
 
< 0.1%
0.996
 
0.1%
17
 
0.1%
1.191
 
< 0.1%
1.247
 
0.1%
1.4918
 
0.2%
ValueCountFrequency (%)
162.351
< 0.1%
121.921
< 0.1%
114.391
< 0.1%
110.171
< 0.1%
101.021
< 0.1%
91.261
< 0.1%
90.491
< 0.1%
86.831
< 0.1%
86.371
< 0.1%
82.861
< 0.1%

Online_Purchase_Amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1128
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.1986583
Minimum0
Maximum4306.42
Zeros5700
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:56.724799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.48
95-th percentile95.4685
Maximum4306.42
Range4306.42
Interquartile range (IQR)7.48

Descriptive statistics

Standard deviation92.3431264
Coefficient of variation (CV)4.809873949
Kurtosis769.5945216
Mean19.1986583
Median Absolute Deviation (MAD)0
Skewness21.76395425
Sum155969.9
Variance8527.252993
MonotonicityNot monotonic
2021-09-27T14:55:57.003882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05700
70.2%
3.9950
 
0.6%
11.9947
 
0.6%
7.9935
 
0.4%
4.4932
 
0.4%
4.9929
 
0.4%
14.9929
 
0.4%
9.9927
 
0.3%
2.9926
 
0.3%
19.9924
 
0.3%
Other values (1118)2125
 
26.2%
ValueCountFrequency (%)
05700
70.2%
0.0115
 
0.2%
0.024
 
< 0.1%
0.051
 
< 0.1%
0.52
 
< 0.1%
0.517
 
0.1%
0.524
 
< 0.1%
0.532
 
< 0.1%
0.82
 
< 0.1%
0.995
 
0.1%
ValueCountFrequency (%)
4306.421
< 0.1%
2808.81
< 0.1%
2142.621
< 0.1%
2033.851
< 0.1%
1652.451
< 0.1%
1513.151
< 0.1%
1071.221
< 0.1%
998.991
< 0.1%
964.771
< 0.1%
956.961
< 0.1%

gender
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
Female
6106 
Male
1987 
Unknown
 
31

Length

Max length7
Median length6
Mean length5.514647957
Min length4

Characters and Unicode

Total characters44801
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female6106
75.2%
Male1987
 
24.5%
Unknown31
 
0.4%

Length

2021-09-27T14:55:57.571496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T14:55:57.749372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
female6106
75.2%
male1987
 
24.5%
unknown31
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e14199
31.7%
a8093
18.1%
l8093
18.1%
F6106
13.6%
m6106
13.6%
M1987
 
4.4%
n93
 
0.2%
U31
 
0.1%
k31
 
0.1%
o31
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36677
81.9%
Uppercase Letter8124
 
18.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e14199
38.7%
a8093
22.1%
l8093
22.1%
m6106
16.6%
n93
 
0.3%
k31
 
0.1%
o31
 
0.1%
w31
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
F6106
75.2%
M1987
 
24.5%
U31
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin44801
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e14199
31.7%
a8093
18.1%
l8093
18.1%
F6106
13.6%
m6106
13.6%
M1987
 
4.4%
n93
 
0.2%
U31
 
0.1%
k31
 
0.1%
o31
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII44801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e14199
31.7%
a8093
18.1%
l8093
18.1%
F6106
13.6%
m6106
13.6%
M1987
 
4.4%
n93
 
0.2%
U31
 
0.1%
k31
 
0.1%
o31
 
0.1%

region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
South East
1680 
North West
1517 
Unknown
866 
South West
769 
West Midlands
658 
Other values (8)
2634 

Length

Max length16
Median length10
Mean length9.596750369
Min length5

Characters and Unicode

Total characters77964
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth West
2nd rowNorth West
3rd rowSouth West
4th rowScotland
5th rowUnknown

Common Values

ValueCountFrequency (%)
South East1680
20.7%
North West1517
18.7%
Unknown866
10.7%
South West769
9.5%
West Midlands658
 
8.1%
East Midlands623
 
7.7%
Scotland615
 
7.6%
North460
 
5.7%
Wales437
 
5.4%
East Anglia344
 
4.2%
Other values (3)155
 
1.9%

Length

2021-09-27T14:55:58.272814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
west2944
21.2%
east2647
19.1%
south2449
17.6%
north1977
14.2%
midlands1281
9.2%
unknown866
 
6.2%
scotland615
 
4.4%
wales437
 
3.1%
anglia344
 
2.5%
ireland135
 
1.0%
Other values (6)190
 
1.4%

Most occurring characters

ValueCountFrequency (%)
t10767
13.8%
s7334
 
9.4%
o6057
 
7.8%
5761
 
7.4%
a5484
 
7.0%
n5138
 
6.6%
h4566
 
5.9%
e3671
 
4.7%
W3381
 
4.3%
d3317
 
4.3%
Other values (17)22488
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter58333
74.8%
Uppercase Letter13870
 
17.8%
Space Separator5761
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t10767
18.5%
s7334
12.6%
o6057
10.4%
a5484
9.4%
n5138
8.8%
h4566
7.8%
e3671
 
6.3%
d3317
 
5.7%
l2837
 
4.9%
u2449
 
4.2%
Other values (7)6713
11.5%
Uppercase Letter
ValueCountFrequency (%)
W3381
24.4%
S3064
22.1%
E2647
19.1%
N2112
15.2%
M1296
 
9.3%
U866
 
6.2%
A344
 
2.5%
I155
 
1.1%
C5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5761
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72203
92.6%
Common5761
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t10767
14.9%
s7334
 
10.2%
o6057
 
8.4%
a5484
 
7.6%
n5138
 
7.1%
h4566
 
6.3%
e3671
 
5.1%
W3381
 
4.7%
d3317
 
4.6%
S3064
 
4.2%
Other values (16)19424
26.9%
Common
ValueCountFrequency (%)
5761
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII77964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t10767
13.8%
s7334
 
9.4%
o6057
 
7.8%
5761
 
7.4%
a5484
 
7.0%
n5138
 
6.6%
h4566
 
5.9%
e3671
 
4.7%
W3381
 
4.3%
d3317
 
4.3%
Other values (17)22488
28.8%

Investment_in_Commudity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3081
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.15274865
Minimum0
Maximum1231.09
Zeros825
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:58.551429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.2825
median23.59
Q349.795
95-th percentile114.642
Maximum1231.09
Range1231.09
Interquartile range (IQR)41.5125

Descriptive statistics

Standard deviation42.47495337
Coefficient of variation (CV)1.174874801
Kurtosis84.3589966
Mean36.15274865
Median Absolute Deviation (MAD)18.19
Skewness4.816122213
Sum293704.93
Variance1804.121663
MonotonicityNot monotonic
2021-09-27T14:55:58.849020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0825
 
10.2%
449
 
0.6%
544
 
0.5%
643
 
0.5%
737
 
0.5%
237
 
0.5%
329
 
0.4%
3.629
 
0.4%
3.927
 
0.3%
825
 
0.3%
Other values (3071)6979
85.9%
ValueCountFrequency (%)
0825
10.2%
0.015
 
0.1%
0.120
 
0.2%
0.121
 
< 0.1%
0.31
 
< 0.1%
0.42
 
< 0.1%
0.411
 
< 0.1%
0.51
 
< 0.1%
0.61
 
< 0.1%
18
 
0.1%
ValueCountFrequency (%)
1231.091
< 0.1%
412.961
< 0.1%
385.241
< 0.1%
384.171
< 0.1%
373.31
< 0.1%
370.391
< 0.1%
342.431
< 0.1%
331.731
< 0.1%
318.851
< 0.1%
318.781
< 0.1%

Investment_in_Equity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2812
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.44247538
Minimum0
Maximum1279.1
Zeros915
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:59.148864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.66
median12.82
Q327.9725
95-th percentile68.4585
Maximum1279.1
Range1279.1
Interquartile range (IQR)23.3125

Descriptive statistics

Standard deviation32.26165951
Coefficient of variation (CV)1.50456787
Kurtosis334.8008243
Mean21.44247538
Median Absolute Deviation (MAD)9.91
Skewness11.52490645
Sum174198.67
Variance1040.814674
MonotonicityNot monotonic
2021-09-27T14:55:59.444976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0915
 
11.3%
3.3355
 
0.7%
247
 
0.6%
542
 
0.5%
1.6738
 
0.5%
2.535
 
0.4%
4.1729
 
0.4%
6.6628
 
0.3%
428
 
0.3%
2.3327
 
0.3%
Other values (2802)6880
84.7%
ValueCountFrequency (%)
0915
11.3%
0.021
 
< 0.1%
0.081
 
< 0.1%
0.098
 
0.1%
0.1718
 
0.2%
0.181
 
< 0.1%
0.211
 
< 0.1%
0.252
 
< 0.1%
0.291
 
< 0.1%
0.339
 
0.1%
ValueCountFrequency (%)
1279.11
< 0.1%
717.741
< 0.1%
556.191
< 0.1%
419.991
< 0.1%
408.641
< 0.1%
368.181
< 0.1%
345.891
< 0.1%
338.761
< 0.1%
316.241
< 0.1%
292.41
< 0.1%

Investment_in_Derivative
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct3269
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.53065239
Minimum0
Maximum1771.16
Zeros445
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2021-09-27T14:55:59.739397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.74
median21.14
Q342.3925
95-th percentile96.0185
Maximum1771.16
Range1771.16
Interquartile range (IQR)33.6525

Descriptive statistics

Standard deviation39.48066024
Coefficient of variation (CV)1.25213585
Kurtosis472.3673829
Mean31.53065239
Median Absolute Deviation (MAD)14.89
Skewness12.46231162
Sum256155.02
Variance1558.722533
MonotonicityNot monotonic
2021-09-27T14:56:00.013955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0445
 
5.5%
3.3340
 
0.5%
539
 
0.5%
229
 
0.4%
1.6729
 
0.4%
5.8326
 
0.3%
2.525
 
0.3%
4.9125
 
0.3%
4.6623
 
0.3%
6.5823
 
0.3%
Other values (3259)7420
91.3%
ValueCountFrequency (%)
0445
5.5%
0.011
 
< 0.1%
0.0910
 
0.1%
0.11
 
< 0.1%
0.178
 
0.1%
0.251
 
< 0.1%
0.336
 
0.1%
0.423
 
< 0.1%
0.57
 
0.1%
0.582
 
< 0.1%
ValueCountFrequency (%)
1771.161
< 0.1%
456.121
< 0.1%
421.551
< 0.1%
411.391
< 0.1%
389.411
< 0.1%
330.281
< 0.1%
319.81
< 0.1%
286.961
< 0.1%
285.761
< 0.1%
276.161
< 0.1%

Portfolio_Balance
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6884
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.3533678
Minimum-78.43
Maximum4283.56
Zeros0
Zeros (%)0.0%
Negative852
Negative (%)10.5%
Memory size63.6 KiB
2021-09-27T14:56:00.325253image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-78.43
5-th percentile-13.54
Q126.2775
median65.56
Q3123.97
95-th percentile271.5595
Maximum4283.56
Range4361.99
Interquartile range (IQR)97.6925

Descriptive statistics

Standard deviation108.3035378
Coefficient of variation (CV)1.212081206
Kurtosis283.2761771
Mean89.3533678
Median Absolute Deviation (MAD)45.695
Skewness8.895471543
Sum725906.76
Variance11729.6563
MonotonicityNot monotonic
2021-09-27T14:56:00.613868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57.95
 
0.1%
71.455
 
0.1%
304
 
< 0.1%
4.094
 
< 0.1%
-9.024
 
< 0.1%
74.994
 
< 0.1%
8.894
 
< 0.1%
89.124
 
< 0.1%
39.414
 
< 0.1%
-9.664
 
< 0.1%
Other values (6874)8082
99.5%
ValueCountFrequency (%)
-78.431
< 0.1%
-77.231
< 0.1%
-76.351
< 0.1%
-73.351
< 0.1%
-72.741
< 0.1%
-69.381
< 0.1%
-67.421
< 0.1%
-66.271
< 0.1%
-64.31
< 0.1%
-64.161
< 0.1%
ValueCountFrequency (%)
4283.561
< 0.1%
1097.441
< 0.1%
1053.81
< 0.1%
1024.681
< 0.1%
952.491
< 0.1%
862.321
< 0.1%
844.241
< 0.1%
790.831
< 0.1%
769.021
< 0.1%
763.221
< 0.1%

Revenue_Grid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.6 KiB
2
7264 
1
860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8124
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
27264
89.4%
1860
 
10.6%

Length

2021-09-27T14:56:01.152635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-27T14:56:01.314149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
27264
89.4%
1860
 
10.6%

Most occurring characters

ValueCountFrequency (%)
27264
89.4%
1860
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8124
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
27264
89.4%
1860
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Common8124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
27264
89.4%
1860
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII8124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27264
89.4%
1860
 
10.6%

Interactions

2021-09-27T14:54:12.326931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:12.635057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:12.932920image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:13.229896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:13.515712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:13.816894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:14.106362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:14.389310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:14.677989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:14.957181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:15.239389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:15.538553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:15.842142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:16.109336image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:16.396439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:16.687221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:16.986399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:17.387329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:17.686732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:18.003082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:18.336044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:18.652114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:18.968660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:19.271469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:19.585693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:19.895175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:20.195616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:20.509283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:20.841739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:21.154375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:21.460761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:21.766503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:22.082757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:22.390507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:22.683544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:22.974197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:23.278240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:23.568799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:23.857296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:24.163261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:24.455573image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:24.751120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:25.040012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:25.323255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:25.612575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:25.920685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:26.395637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:26.671562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:26.961283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:27.250806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:27.553257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:27.827342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:28.112434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:28.419653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:28.710301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:28.999306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:29.295979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:29.585138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:29.873343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:30.150278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:30.434062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:30.718096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:31.016673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:31.311241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:31.582078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:31.865276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:32.150791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:32.448305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:32.723362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:33.039714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:33.372715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:33.673340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:33.989106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:34.322186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:34.625215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:34.925366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:35.232609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:35.523085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:35.823933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:36.145368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:36.464987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:36.754040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:37.253452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:37.560426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:37.876579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:38.178386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:38.461950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:38.767995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:39.052590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:39.335882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:39.633924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:39.914141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:40.200130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:40.488684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:40.757658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:41.038861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:41.336601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:41.634176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:41.902168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:42.198309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:42.475117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:42.774241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:43.041801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:43.331617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:43.640390image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:43.930042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:44.213262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:44.516446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:44.801625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:45.089751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:45.381702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:45.662094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:45.945876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:46.243867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:46.544858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:46.812366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:47.096061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:47.388241image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:47.680912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:47.959987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:48.243275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:48.550467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:48.834988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:49.126410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:49.420467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:49.701783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:49.993584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:50.525448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:50.799222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:51.080750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:51.390511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:51.695779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:51.965924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:52.248706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:52.533200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:52.827691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:53.098582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:53.372017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:53.664293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:53.941742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:54.221477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:54.509581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:54.778849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:55.055431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:55.326026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:55.590376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:55.854545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:56.142247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:56.419804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:56.678715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:56.952140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:57.226939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:57.519419image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:57.785815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:58.056684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:58.342905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:58.626824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:58.900477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:59.195321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:59.476315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:54:59.749871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:00.030531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:00.299848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:00.570469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:00.865977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:01.150664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:01.415568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:01.694733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:01.969623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:02.249972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:02.516237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:02.817495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:03.139791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:03.446724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:03.756627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:04.079252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:04.386836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:04.699661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:05.011833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:05.311529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:05.612334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:05.936297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:06.560547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:06.862196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:07.169144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:07.485504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:07.792984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:08.093855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:08.400476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:08.718677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:09.034155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:09.335636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:09.649797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:09.969998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:10.275024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:10.575027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:10.865248image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:11.164027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:11.473305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:11.792003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:12.080290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:12.389211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:12.688384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:12.998108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:13.296056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:13.565353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:13.847108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:14.107990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:14.371805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:14.653686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:14.920028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:15.187667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:15.453872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:15.710887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:15.968879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:16.258386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:16.527921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:16.778139image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:17.042384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:17.311099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:17.580637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:17.834214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:18.117010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:18.426550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:18.717050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:19.002844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:19.301329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:19.599144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:19.923628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:20.207988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:20.490458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:20.766605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:21.076015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:21.374944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:21.656947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:21.947085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:22.235285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:22.536849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:22.811362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:23.093109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:23.400046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:23.681733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:23.973210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:24.270657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:24.568106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:24.848422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:25.135555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:25.426854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:25.701746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:26.376381image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:26.679608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:26.949817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:27.246503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:27.538855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:27.829511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:28.102277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:28.399595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:28.707762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:29.015527image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:29.315414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:29.634164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:29.925968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:30.219037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:30.522277image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:30.812070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:31.106761image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:31.421962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:31.733872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:32.013243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:32.312974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:32.612780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:32.920999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:33.212421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:33.487575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:33.769700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:34.029312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:34.289567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:34.569443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:34.835938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:35.103437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:35.362685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:35.626683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:35.888363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:36.168219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:36.451159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:36.702716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:36.971917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:37.238367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T14:55:37.517493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-27T14:56:01.512044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-27T14:56:02.188100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-27T14:56:02.878062image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-27T14:56:03.610891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-27T14:56:04.451631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-27T14:55:38.142046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-27T14:55:41.498091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

REF_NOchildrenage_bandstatusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partneryear_last_movedTVareapost_codepost_areaAverage_Credit_Card_TransactionBalance_TransferTerm_DepositLife_InsuranceMedical_InsuranceAverage_A/C_BalancePersonal_LoanInvestment_in_Mutual_FundInvestment_Tax_Saving_BondHome_LoanOnline_Purchase_AmountgenderregionInvestment_in_CommudityInvestment_in_EquityInvestment_in_DerivativePortfolio_BalanceRevenue_Grid
05466231-35PartnerProfessionalProfessionalOwn Home>=35,000NoNo1981MeridianM51 0GUM5126.9829.99312.25299.7988.72108.85175.43134.358.9855.447.68FemaleNorth West151.5581.79136.02360.372
19091Zero45-50PartnerSecretarial/AdminProfessionalOwn Home>=35,000NoNo1997MeridianL40 2AGL4035.9874.480.0099.9610.9948.4515.990.000.000.0018.99FemaleNorth West44.2813.9129.2389.222
29744136-40PartnerManual WorkerManual WorkerRent Privately<22,500, >=20,000YesYes1996HTVTA19 9PTTA190.0024.460.0018.440.000.000.0210.460.000.000.00FemaleSouth West8.581.754.8214.502
310700231-35PartnerManual WorkerManual WorkerOwn Home<25,000, >=22,500NoNo1990Scottish TVFK2 9NGFK244.990.000.000.0029.990.000.000.000.000.000.00FemaleScotland15.000.005.0068.982
41987Zero55-60PartnerHousewifeProfessionalOwn Home>=35,000NoNo1989YorkshireLS23 7DJLS230.000.000.000.000.000.000.009.980.000.000.00FemaleUnknown0.001.661.661.882
53309Zero45-50PartnerSecretarial/AdminBusiness ManagerOwn Home>=35,000NoNo1984UlsterBT17 9NABT179.490.010.000.5155.890.0028.980.000.000.000.00FemaleNorthern Ireland13.184.8314.2333.622
66610Zero36-40PartnerSecretarial/AdminSecretarial/AdminOwn Home<30,000, >=27,500YesNo1986CentralB62 8TFB629.990.000.000.000.0026.9622.9980.421.003.995.49FemaleWest Midlands2.0023.4821.9013.122
710621Zero61-65PartnerRetiredRetiredOwn Home<20,000, >=17,500NoNo1998GranadaPR8 2TYPR80.000.000.000.000.000.000.0029.950.000.000.00MaleNorth West0.004.994.9915.742
82630145-50PartnerProfessionalProfessionalOwn Home>=35,000NoNo1980UnknownCF15 9THCF150.0082.960.0040.4712.490.0028.970.000.000.000.00FemaleUnknown27.184.8313.6636.052
99356336-40PartnerProfessionalHousewifeOwn Home<27,500, >=25,000YesNo1997MeridianM13 9BGM130.000.000.000.000.000.0015.990.0024.470.000.00MaleNorth West0.006.746.748.602

Last rows

REF_NOchildrenage_bandstatusoccupationoccupation_partnerhome_statusfamily_incomeself_employedself_employed_partneryear_last_movedTVareapost_codepost_areaAverage_Credit_Card_TransactionBalance_TransferTerm_DepositLife_InsuranceMedical_InsuranceAverage_A/C_BalancePersonal_LoanInvestment_in_Mutual_FundInvestment_Tax_Saving_BondHome_LoanOnline_Purchase_AmountgenderregionInvestment_in_CommudityInvestment_in_EquityInvestment_in_DerivativePortfolio_BalanceRevenue_Grid
81149449145-50PartnerRetiredHousewifeOwn Home<12,500, >=10,000NoNo1982YorkshireDN3 3LDDN3148.45104.9827.9999.460.0119.990.000.000.000.000.00MaleUnknown76.183.3319.91116.451
81156341236-40PartnerSecretarial/AdminBusiness ManagerOwn Home<27,500, >=25,000NoNo1986Tyne TeesNE9 7BNNE90.0091.460.00102.440.0028.4660.4688.380.000.0017.48FemaleNorth38.7832.4646.62114.382
81165043Zero61-65PartnerRetiredRetiredOwn Home< 8,000, >= 4,000NoNo1982CarltonSS4 1QASS40.0025.490.000.000.0035.980.000.0011.986.480.00FemaleSouth East5.109.077.99-7.372
8117526126-30PartnerSecretarial/AdminProfessionalOwn Home>=35,000NoNo1998Scottish TVKY12 7YBKY120.0067.9795.4622.9930.9581.450.0049.940.001.490.00FemaleScotland43.4722.1530.8991.372
81187120Zero45-50PartnerProfessionalSecretarial/AdminOwn Home>=35,000NoNo1981GranadaSK9 2ESSK90.0015.9954.98137.440.0088.925.49137.320.0027.9498.92MaleNorth West41.6859.7761.53210.662
81196516336-40PartnerManual WorkerHousewifeOwn Home<20,000, >=17,500NoNo1981MeridianL33 2TWL330.000.000.000.000.000.000.000.000.000.000.00MaleNorth West0.000.000.0015.232
81205897Zero61-65WidowedRetiredUnknownOwn Home< 8,000, >= 4,000NoNo1960CentralDE6 5GYDE60.000.009.490.000.002.990.0046.760.000.000.00FemaleEast Midlands1.908.298.2968.422
81216130141-45Single/Never MarriedHousewifeUnknownRent from Council/HA< 8,000, >= 4,000NoNo1987UlsterBT28 1DXBT280.00107.420.0023.4223.9938.950.00101.371.003.720.00FemaleNorthern Ireland30.9724.1731.46106.062
8122980Zero61-65PartnerRetiredRetiredOwn Home< 4,000NoNo1985MeridianCT10 2JFCT100.0059.480.000.000.000.000.000.000.000.000.00FemaleSouth East11.900.000.00-9.192
81238267341-45PartnerBusiness ManagerHousewifeOwn Home<25,000, >=22,500NoNo1974Tyne TeesTS16 0HGTS1673.9674.4742.98127.4513.4951.4695.9058.4415.474.493.99MaleNorth66.4738.2960.37160.792